sol=0
jpl=0
bollyn=bool(sol)
if bollyn == True:
    print('Downloading')
    if jpl == 0:
        !pip install kagglehub huggingface_hub ipython-sql scipy
        print('Downloaded with pip')
    elif jpl ==1:
        softs=["kagglehub","seaborn","huggingface_hub","ipython-sql","scipy"]
        print('Downloading with piplite')
        import piplite
        for s in softs:
            print(s)
            await piplite.install(s)
            print(s, 'installed')
        print('Done')
        
else:
    print('Libraries already downloaded')

#await piplite.install("pretty_jupyter")
import kagglehub
import warnings
import pandas as pd
import prettytable
prettytable.DEFAULT = 'DEFAULT'
import sqlite3
warnings.simplefilter(action='ignore', category=FutureWarning)
# Download latest version
import pandas as pd
import os
from matplotlib import pyplot as plt

# The two datasets are downloaded with with API resources
path = kagglehub.dataset_download("adilshamim8/salary-prediction-salary-dataset")
fls=os.listdir(path)
df=pd.read_csv(path+'/'+fls[0])
dfTemp = pd.read_csv("hf://datasets/hugginglearners/data-science-job-salaries/ds_salaries.csv")

# Tables and connections are created for the SQL tables
%reload_ext sql
con=sqlite3.connect('KaggleHF_DS.db')
cursor=con.cursor()
%sql sqlite:///KaggleHF_DS.db
df.to_sql("salary_prediction_updated_datas",con,if_exists='replace', index=False)
dfTemp.to_sql("ds_salaries",con,if_exists='replace', index=True)

# Query for THF's to check it's loaded
%sql Select distinct * from ds_salaries limit 5;

# Query for Kaggle's to check it's loaded
%sql Select distinct * from salary_prediction_updated_datas limit 5;

import seaborn as sns
yrs=pd.read_sql_query("select work_year from ds_salaries where job_title like 'Data Scientist'",con)
AllDS=pd.read_sql_query("select * from ds_salaries where job_title like 'Data Scientist'",con)
AllCs=AllDS.columns
Years=pd.unique(yrs['work_year'])
bxs=pd.DataFrame()
for y,yr in enumerate(Years):
    bxyr=pd.read_sql_query(f"select salary_in_usd from ds_salaries where job_title like 'Data Scientist' and work_year = {yr} ",con)
    bxs.loc[0:len(bxyr['salary_in_usd']),f'{yr}']=bxyr.loc[:,'salary_in_usd']/12
boxes=pd.DataFrame(bxs)

# Plot the orbital period with horizontal boxes
#sns.boxplot(data=boxes)

# Add in points to show each observation

import operator
places=[2,8,9]
getter=operator.itemgetter(*places)
cols=list(getter(AllCs))

dbsns=AllDS[cols]

fig,axes=plt.subplots();
#sns.swarmplot(data=dbsns,x=dbsns['work_year'],y=dbsns['salary_in_usd'],hue='employee_residence')
ax=sns.violinplot(data=bxs)
for violin, alpha in zip(ax.collections[::1], [0.8,0.6,0.4,0.2]):
    violin.set_alpha(.25)
#axes.plot(mng,color='k',)
sns.boxplot(data=dbsns,x=dbsns['work_year'],y=dbsns['salary_in_usd']/12,hue='employee_residence')
#sns.swarmplot(data=boxes)
sns.despine(offset=10, trim=True)
plt.ylabel('Salary USD')
plt.xlabel('Year');
# Improve the legend
sns.move_legend(
    axes, loc="upper right", ncol=6, frameon=False, columnspacing=1, handletextpad=0,
)
#Boxes=pd.DataFrame.groupby(by=TempDS['work_year'])
#TempDS.boxplot(column=Boxes,by=TempDS['salary_in_usd'])

DSt=bxs.describe()
samples=DSt[DSt.columns].loc['count']
means=DSt[DSt.columns].loc['mean']
DSt

from scipy.stats import f_oneway
f, p = f_oneway(bxs['2020'],bxs['2021'],bxs['2022'])
print(f"A comparison between the analyzed periods with ANOVA says it {'is significant' if p<0.05 else 'is not significant'}, with p= {round(p,4)}")

%sql alter table salary_prediction_updated_datas rename 'Job Role' to Job;
%sql alter table salary_prediction_updated_datas rename 'Education Level' to Education;
%sql update salary_prediction_updated_datas set salary = salary/12;

careers=pd.read_sql_query("select Distinct education from salary_prediction_updated_datas",con)
levs=[]
for carre in careers['Education']:
    vs=pd.read_sql_query(f"select * from salary_prediction_updated_datas where Education like '%{carre[0:3]}%' AND Job = 'Data Scientist' limit 1",con)
    print()
    levs.append({"Years of Experience":vs.loc[0,"YearsExperience"],"Degree":vs.loc[0,"Education"],"Job Title":vs.loc[0,"Job"],"Average month salary":round(vs.loc[0,"Salary"],2)})
T=pd.DataFrame(levs)
print(T)

# We need to extract the data from the table. 
# One way to do wo is by extracting the queries from the tables as shown below
import re
phds=pd.read_sql_query("select * from salary_prediction_updated_datas where Education like 'PhD' AND Job = 'Data Scientist'",con);
bach=pd.read_sql_query("select * from salary_prediction_updated_datas where Education like '%Bachelor%' AND Job LIKE \'%Data%\'",con);
mast=pd.read_sql_query("select * from salary_prediction_updated_datas where Education like '%Master%' AND Job LIKE '%Data%'",con);
allDS=pd.read_sql_query("select * from salary_prediction_updated_datas where Job = 'Data Scientist'",con);
allDS2=cursor.execute("select * from salary_prediction_updated_datas where Job LIKE '%Data%'")
allDS2=pd.DataFrame(cursor.fetchall())
allDS2.rename(columns={0:"Index",1:"YearsExperience",2:"Education Level",3:"Job Role",4:"Salary"});
DSs=df[df['Job Role']=="Data Scientist"]
DSs.loc[:,'Salary']=DSs.loc[:,'Salary']/12
Baches=DSs[DSs['Education Level']=="Bachelor's"]
Masters=DSs[DSs['Education Level']=="Master's"]
Docs=DSs[DSs['Education Level']=="PhD"]

#import libraries
from matplotlib import pyplot as plt
import seaborn as sb
import numpy as np
import scipy
print('Libs uploaded')

#Plotted the Salary vs the years of experience

#Create a joint plot
dtpt=DSs
fig= plt.figure
j=sb.jointplot(data=dtpt,x='YearsExperience',y='Salary',ratio=10,hue='Education Level',marker='+',marginal_ticks=True)
x=np.array(dtpt['YearsExperience'])
y=np.array(dtpt['Salary'])
X=x[np.isnan(x)==False]
Y=y[np.isnan(y)==False]
minl=min([len(X),len(Y)])
X=X[0:minl]
Y=Y[0:minl]
# Obtained correlation coefficients for the data
corrs=scipy.stats.pearsonr(X,Y)
r,p=scipy.stats.pearsonr(X,Y)

# Obtained the  slope and intercept as a measur for initial salaries from (x,y)

def estimate_coef(x, y):
    # number of observations/points
    n = np.size(x)

    # mean of x and y vector
    m_x = np.mean(x)
    m_y = np.mean(y)

    # calculating cross-deviation and deviation about x
    SS_xy = np.sum(y*x) - n*m_y*m_x
    SS_xx = np.sum(x*x) - n*m_x*m_x

    # calculating regression coefficients
    b_1 = SS_xy / SS_xx
    b_0 = m_y - b_1*m_x
    return (b_0, b_1)

intercept,slope=estimate_coef(X,Y)
r,p=scipy.stats.pearsonr(X,Y)
#otro, = j.ax_joint.plot([], [], linestyle="", alpha=0)
#j.ax_joint.legend([otro],['y=mx+b; y={:3.2f}x+{:3.2f}, r={:.3}, p={:2.3e}'.format(b_1,b_0,r,p)])
#j.ax_joint.legend([otro],['r={:.3}, p={:.3e}'.format(b_1,b_0,r,p)])
plt.suptitle('Data Scientists\' Salary');

print(['Linear regression: y=mx+b; y={:3.2f}x+{:3.2f}, r={:.3}, p={:.4e}'.format(intercept,slope,r,p)])

numofClasses=pd.read_sql_query("select Education, count(*) as freq from salary_prediction_updated_datas where Job = 'Data Scientist' group by Education",con);
numofClasses

TablaDeg=[]
for degree in numofClasses['Education']:
    base=DSs[DSs['Education Level']==degree]

    x=np.array(base['YearsExperience'])
    y=np.array(base['Salary'])
    X=x[np.isnan(x)==False]
    Y=y[np.isnan(y)==False]
    minl=min([len(X),len(Y)])
    X=X[0:minl]
    Y=Y[0:minl]

    
    r,p=scipy.stats.pearsonr(X,Y)
    intercept,slope=estimate_coef(X,Y)
    TablaDeg.append({"Degree":degree,"n=":x.shape[0],"Raise":slope,"Initial":intercept,"R\N{superscript two}":r,"P-value":round(p,4)})
Tabla=pd.DataFrame(TablaDeg)        
  

print(Tabla)
fig, ax=plt.subplots()
ini=ax.plot(Tabla['Degree'],Tabla['Initial'], color='gray',label='Initial')#,
plt.ylabel('Initial salary (monthly)');
plt.xticks(rotation=15);
slp=plt.twinx()
rs=slp.plot(Tabla['Degree'],Tabla['Raise'],color='Blue',label='Raise')
fig.suptitle('Salary per education level');
plt.ylabel('Raise/year');
#plt.legend([ini,rs],['Initial','Raise'])

try:
    !jupyter nbconvert dsp001.ipynb --to html --template pj
except Exception as e:
    print('HTML not stored')